HSV Genital Skin Biopsy scRNAseq Explorer
By: Joe Hou

Overview
This Shiny application provides an interactive interface for
exploring 10X scRNAseq data derived from HSV+ skin biopsy samples. https://hsvdashboard.joehou.net/.
As the manuscript is currently under review for publication, the raw and
processed data remain undisclosed.
- About the
cohort:
This study involved healthy adults with confirmed herpes simplex
virus type 2 (HSV-2) seropositivity, verified via Western blot analysis.
All participants tested negative for HIV. In this longitudinal HSV
study, we enrolled 17 participants and collected genital skin biopsies
at three distinct time points to capture HSV shedding and healing
phases.
- About the time
points:
Samples were collected at three key stages: the first, labeled
“Prior,” was collected before any visible lesions appeared. When lesions
became visible, samples were labeled as “Lesion.” Finally, samples taken
8 weeks after lesion appearance are referred to as “Post.”
- About the
dataset:
This release includes datasets with refined mapping for T cells,
Myeloid cells and Visium data. Users can freely select from the
available datasets for exploration. We recommend using the
“CellType_Level3” annotation for visualization, as it provides the most
detailed and granular clustering. While “CellType_Level1” offers a
broader categorization, “CellType_Level3” represents the final, detailed
clustering, with cell types identified at a higher level of specificity.
Full definitions of each cell cluster and cell type will be provided in
the manuscript upon publication.
Features
and Functionalities
Two main tabs are provided for scRNAseq and
spatial Visium data.
For scRNAseq data:
Two separate tabs are provided for data exploration, with a focus
on:
- Cell Cluster Composition:
- Goal: Analyze the composition of cell clusters
across time points and individuals, with some clusters showing
specificity to time points and variability across participants.
- Gene Expression Patterns:
- Goal: Investigate gene expression patterns across
different cell clusters, including gene distribution and expression
variability across time points.
For Visium data:
- Gene Expression Patterns:
- Goal: Investigate gene expression distribution and
intensity across tissue.
- Cell Type De-convolution:
- Goal: To further understand cell type composition
per spatial location.
Interactive Exploration on Cell Type
(Cluster Discovery Tab)
- Cell Type and Status Identification: Users can view
all available cell types and subjects, with the flexibility to select
specific ones for detailed exploration.
- Dynamic UMAP Visualization: Initial displays show
holistic UMAP results, which can be refined by time point. Selecting
different subjects and cell types dynamically updates the UMAP to show
specific clusters.
- Cell Type Statistics: Displays percentages and
counts of cell types for selected or all subjects.
- Cell Type by Subject: Visualizes the percentage of
each cell type within a given sample using stacked bar plots to
understand the heterogeneity of cell type acroos samples
Interactive Exploration on Gene
Expression (Gene Discovery Tab)
- Feature Exploration: Allows for the selection of
cell types, subjects, and specific genes of interest.
- At Single Cell Level:
- Feature Gene Highlight: The expression of selected
gene projected to UMAP overlay with cell type annotation, vividly
display gene expression pattern across cell types.
- Heatmap: Shows selected gene expression intensity
per cell, grouped by cell types, statuses, and subject, aiding in
providing gene details per sample.
- At Cell Type Level:
- Violin Plots: The selected gene expression was
summarized into cell type level and shows average expression levels
across cell types and statuses, aiding in understanding temporal
dynamics.
- Feature Percentage Plots: Summarizes gene
expression at the sample/subject level, indicating the proportion of
cells expressing a specific gene within a cell type.
- Dot Plots: Dot size indicates the percentage of
cells expressing each gene, while color intensity represents gene
expression levels.
Tech
Note
- HDF5 Array File System: Utilized to prevent RAM
overload by saving massive data in HDF5 files and processing them
directly from disk. This approach enhances memory efficiency when
handling large datasets.
- Docker Image: The application is containerized
using Docker to ensure consistency across different platforms. This
allows for a reliable and reproducible environment for all users. You
can run command line below to get this container runs locally: finch run
-it –rm -p 7777:3838 jhoufred/hsv-dashboard-image:2.0 or this: docker
run -d -p 7777:3838 jhoufred/hsv-dashboard-image:2.0
- AWS EC2 Backend: The application is deployed on AWS
EC2, providing a scalable and robust backend infrastructure for
performance and reliability.
Version2
Update
- NEW AWS Architecture: Elastic Container Registry
(ECR), Elastic Container Service (ECS), Application Load Balancer (ALB),
Auto Scaling, and Route 53
Gallery
Here are some visual showcases of the application features:
Version Release
Nov-1-2024: release ver 1.0